Effectiveness of Automatic Planning of Fronto-orbital Advancement for the Surgical Correction of Metopic Craniosynostosis

The surgical correction of metopic craniosynostosis usually relies on the subjective judgment of surgeons to determine the configuration of the cranial bone fragments and the degree of overcorrection. This study evaluates the effectiveness of a new approach for automatic planning of fronto-orbital advancement based on statistical shape models and including overcorrection.The authors have no financial interest in relation to the content of this article. This work was supported by grants R42 HD081712 (Eunice Kennedy Shriver National Institute of Child Health and Human Development), K99DE027993 (National Institute of Dental and Craniofacial Research), and PI18/01625 (Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III and European Regional Development Fund “Una manera de hacer Europa”)

Robust head CT image registration pipeline for craniosynostosis skull correction surgery

Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of \u3c2.5 mm in the targeted skull region across both the normal subjects and patients. Keywords: image registration, bone, surgery, medical image processing, computerised tomography, deformation, biomechanics, image resolution, optimisation Keywords: robust head CT image registration pipeline, craniosynostosis skull correction surgery, congenital malformation, infant skull, corrective surgery, deformation, optimal correction strategy, patient-specific skull model extraction, presurgical computed tomography image, robust multistage multiresolution registration pipeline, patient-specihc CT image, normal CT images, initial optimisation, very low resolution, mean surface-to-surface distance, template skull, targeted skull regio

Optimization of craniosynostosis surgery: virtual planning, intraoperative 3D photography and surgical navigation

Mención Internacional en el título de doctorCraniosynostosis is a congenital defect defined as the premature fusion of one or more cranial sutures. This fusion leads to growth restriction and deformation of the cranium, caused by compensatory expansion parallel to the fused sutures. Surgical correction is the preferred treatment in most cases to excise the fused sutures and to normalize cranial shape. Although multiple technological advancements have arisen in the surgical management of craniosynostosis, interventional planning and surgical correction are still highly dependent on the subjective assessment and artistic judgment of craniofacial surgeons. Therefore, there is a high variability in individual surgeon performance and, thus, in the surgical outcomes. The main objective of this thesis was to explore different approaches to improve the surgical management of craniosynostosis by reducing subjectivity in all stages of the process, from the preoperative virtual planning phase to the intraoperative performance. First, we developed a novel framework for automatic planning of craniosynostosis surgery that enables: calculating a patient-specific normative reference shape to target, estimating optimal bone fragments for remodeling, and computing the most appropriate configuration of fragments in order to achieve the desired target cranial shape. Our results showed that automatic plans were accurate and achieved adequate overcorrection with respect to normative morphology. Surgeons’ feedback indicated that the integration of this technology could increase the accuracy and reduce the duration of the preoperative planning phase. Second, we validated the use of hand-held 3D photography for intraoperative evaluation of the surgical outcome. The accuracy of this technology for 3D modeling and morphology quantification was evaluated using computed tomography imaging as gold-standard. Our results demonstrated that 3D photography could be used to perform accurate 3D reconstructions of the anatomy during surgical interventions and to measure morphological metrics to provide feedback to the surgical team. This technology presents a valuable alternative to computed tomography imaging and can be easily integrated into the current surgical workflow to assist during the intervention. Also, we developed an intraoperative navigation system to provide real-time guidance during craniosynostosis surgeries. This system, based on optical tracking, enables to record the positions of remodeled bone fragments and compare them with the target virtual surgical plan. Our navigation system is based on patient-specific surgical guides, which fit into the patient’s anatomy, to perform patient-to-image registration. In addition, our workflow does not rely on patient’s head immobilization or invasive attachment of dynamic reference frames. After testing our system in five craniosynostosis surgeries, our results demonstrated a high navigation accuracy and optimal surgical outcomes in all cases. Furthermore, the use of navigation did not substantially increase the operative time. Finally, we investigated the use of augmented reality technology as an alternative to navigation for surgical guidance in craniosynostosis surgery. We developed an augmented reality application to visualize the virtual surgical plan overlaid on the surgical field, indicating the predefined osteotomy locations and target bone fragment positions. Our results demonstrated that augmented reality provides sub-millimetric accuracy when guiding both osteotomy and remodeling phases during open cranial vault remodeling. Surgeons’ feedback indicated that this technology could be integrated into the current surgical workflow for the treatment of craniosynostosis. To conclude, in this thesis we evaluated multiple technological advancements to improve the surgical management of craniosynostosis. The integration of these developments into the surgical workflow of craniosynostosis will positively impact the surgical outcomes, increase the efficiency of surgical interventions, and reduce the variability between surgeons and institutions.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Norberto Antonio Malpica González.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Tamas Ung

Doctor of Philosophy

dissertationStatistical analysis of time dependent imaging data is crucial for understanding normal anatomical development as well as disease progression. The most promising studies are of longitudinal design, where repeated observations are obtained from the same subjects. Analysis in this case is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. In any case, the study of anatomical change over time has the potential to further our understanding of many dynamic processes. What is needed are accurate computational models to capture, describe, and quantify anatomical change over time. Anatomical shape is encoded in a variety of representations, such as medical imaging data and derived geometric information extracted as points, curves, and/or surfaces. By considering various shape representations embedded into the same ambient space as a shape complex, either in 2D or 3D, we obtain a more comprehensive description of the anatomy than provided by an single isolated shape. In this dissertation, we develop spatiotemporal models of anatomical change designed to leverage multiple shape representations simultaneously. Rather than study directly the geometric changes to a shape itself, we instead consider how the ambient space deforms, which allows all embedded shapes to be included simultaneously in model estimation. Around this idea, we develop two complementary spatiotemporal models: a flexible nonparametric model designed to capture complex anatomical trajectories, and a generative model designed as a compact statistical representation of anatomical change. We present several ways spatiotemporal models can support the statistical analysis of scalar measurements, such as volume, extracted from shape. Finally, we cover the statistical analysis of higher dimensional shape features to take better advantage of the rich morphometric information provided by shape, as well as the trajectory of change captured by spatiotemporal models

Craniosynostosis surgery: workflow based on virtual surgical planning, intraoperative navigation and 3D printed patient-specific guides and templates

Craniosynostosis must often be corrected using surgery, by which the affected bone tissue is remodeled. Nowadays, surgical reconstruction relies mostly on the subjective judgement of the surgeon to best restore normal skull shape, since remodeled bone is manually placed and fixed. Slight variations can compromise the cosmetic outcome. The objective of this study was to describe and evaluate a novel workflow for patient-specific correction of craniosynostosis based on intraoperative navigation and 3D printing. The workflow was followed in five patients with craniosynostosis. Virtual surgical planning was performed, and patient-specific cutting guides and templates were designed and manufactured. These guides and templates were used to control osteotomies and bone remodeling. An intraoperative navigation system based on optical tracking made it possible to follow preoperative virtual planning in the operating room through real-time positioning and 3D visualization. Navigation accuracy was estimated using intraoperative surface scanning as the gold-standard. An average error of 0.62 mm and 0.64 mm was obtained in the remodeled frontal region and supraorbital bar, respectively. Intraoperative navigation is an accurate and reproducible technique for correction of craniosynostosis that enables optimal translation of the preoperative plan to the operating room. © 2019, The Author(s).This work has been supported by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, project “PI18/01625”, co-funded by European Regional Development Fund (ERDF), “A way of making Europe”

New Technologies to Improve Surgical Outcome during Open-Cranial Vault Remodeling

Current approaches for the surgical correction of craniosynostosis are highly dependent on surgeon experience. Therefore, outcomes are often inadequate, causing suboptimal esthetic results. Novel methods for cranial shape analysis based on statistical shape models enable accurate and objective diagnosis from preoperative 3D photographs or computed tomography scans. Moreover, advanced algorithms are now available to calculate a reference cranial shape for each patient from a multi-atlas of healthy cases, and to determine the most optimal approach to restore normal calvarial shape. During surgery, multiple technologies are available to ensure accurate translation of the preoperative virtual plan into the operating room. Patient-specific cutting guides and templates can be designed and manufactured to assist during osteotomy and remodeling. Then, intraoperative navigation and augmented reality visualization can provide real-time guidance during the placement and fixation of the remodeled bone. Finally, 3D photography enables intraoperative surgical outcome evaluation and postoperative patient follow-up. This chapter summarizes recent literature on all these technologies, showing how their integration into the surgical workflow could increase reproducibility and reduce inter-surgeon variability in open cranial vault remodeling procedures

Data-Driven Classification Methods for Craniosynostosis Using 3D Surface Scans

Diese Arbeit befasst sich mit strahlungsfreier Klassifizierung von Kraniosynostose mit zusätzlichem Schwerpunkt auf Datenaugmentierung und auf die Verwendung synthetischer Daten als Ersatz für klinische Daten. Motivation: Kraniosynostose ist eine Erkrankung, die Säuglinge betrifft und zu Kopfdeformitäten führt. Diagnose mittels strahlungsfreier 3D Oberflächenscans ist eine vielversprechende Alternative zu traditioneller computertomographischer Bildgebung. Aufgrund der niedrigen Prävalenz und schwieriger Anonymisierbarkeit sind klinische Daten nur spärlich vorhanden. Diese Arbeit adressiert diese Herausforderungen, indem sie neue Klassifizierungsalgorithmen vorschlägt, synthetische Daten für die wissenschaftliche Gemeinschaft erstellt und zeigt, dass es möglich ist, klinische Daten vollständig durch synthetische Daten zu ersetzen, ohne die Klassifikationsleistung zu beeinträchtigen. Methoden: Ein Statistisches Shape Modell (SSM) von Kraniosynostosepatienten wird erstellt und öffentlich zugänglich gemacht. Es wird eine 3D-2D-Konvertierung von der 3D-Gittergeometrie in ein 2D-Bild vorgeschlagen, die die Verwendung von Convolutional Neural Networks (CNNs) und Datenaugmentierung im Bildbereich ermöglicht. Drei Klassifizierungsansätze (basierend auf cephalometrischen Messungen, basierend auf dem SSM, und basierend auf den 2D Bildern mit einem CNN) zur Unterscheidung zwischen drei Pathologien und einer Kontrollgruppe werden vorgeschlagen und bewertet. Schließlich werden die klinischen Trainingsdaten vollständig durch synthetische Daten aus einem SSM und einem generativen adversarialen Netz (GAN) ersetzt. Ergebnisse: Die vorgeschlagene CNN-Klassifikation übertraf konkurrierende Ansätze in einem klinischen Datensatz von 496 Probanden und erreichte einen F1-Score von 0,964. Datenaugmentierung erhöhte den F1-Score auf 0,975. Zuschreibungen der Klassifizierungsentscheidung zeigten hohe Amplituden an Teilen des Kopfes, die mit Kraniosynostose in Verbindung stehen. Das Ersetzen der klinischen Daten durch synthetische Daten, die mit einem SSM und einem GAN erstellt wurden, ergab noch immer einen F1-Score von über 0,95, ohne dass das Modell ein einziges klinisches Subjekt gesehen hatte. Schlussfolgerung: Die vorgeschlagene Umwandlung von 3D-Geometrie in ein 2D-kodiertes Bild verbesserte die Leistung bestehender Klassifikatoren und ermöglichte eine Datenaugmentierung während des Trainings. Unter Verwendung eines SSM und eines GANs konnten klinische Trainingsdaten durch synthetische Daten ersetzt werden. Diese Arbeit verbessert bestehende diagnostische Ansätze auf strahlungsfreien Aufnahmen und demonstriert die Verwendbarkeit von synthetischen Daten, was klinische Anwendungen objektiver, interpretierbarer, und weniger kostspielig machen

Evaluation System for Craniosynostosis Surgeries with Computer Simulation and Statistical Modelling

Craniosynostosis is a pathology in infants when one or more sutures prematurely closed, leading to abnormal skull shape. It has been classified according to the specific suture that has been closed, each of which has a typical skull shape. Surgery is the common treatment to correct the deformed skull shape and to reduce the excessive intracranial pressure. Since every case is unique, the cranial facial teams have difficulties to select an optimum solution for a specific patient from multiple options. In addition, there is not an appropriate quantified measurement existed currently to help cranial facial team to quantitatively evaluate their surgeries. We aimed to develop a head model of a craniosynostosis patient, which allows neurosurgeons to perform any potential surgeries on it so as to simulate the postoperative head development. Therefore, neurosurgeons could foresee the surgical results and is able to select the optimal one. In this thesis, we have developed a normal head model, and built mathematical models for possible dynamic behaviors. We also modified this model by closing one or two sutures to simulate common types of craniosynostosis. The abnormal simulation results showed a qualitative match with real cases and the normal simulation indicated a higher growth rate of cranial index than clinical data. We believed that this discrepancy caused by the rigidity of our skull plates, which will be adapted to deformable object in the future. In order to help neurosurgeons to better evaluate a surgery, we hope to develop an algorithm to quantify the level of deformity of a skull. We have designed a set of work flow and targeted curvatures as the key role. A training data was carefully selected to search for an optimal system to characterize different shapes. A set of test data was used to validate our algorithm to assess the performance of the optimal system. With a stable evaluating system, we can evaluate a surgery by comparing the preoperative and postoperative skulls from the patient. An effective surgery can be considered if the postoperative skull shifted toward normal shape from preoperative shape

Automated Planning with Multivariate Shape Descriptors for Fibular Transfer in Mandibular Reconstruction

Objective: This paper introduces methods to automate preoperative planning of fibular segmentation and placement for mandibular reconstruction with fibular flaps. Methods: Preoperative virtual planning for this type of surgery has been performed by manual adjustment of many parameters, or based upon a single feature of the reconstruction. We propose a novel planning procedure formulated as a non-convex minimization problem of an objective function using the multilateral shape descriptors. Results: A retrospective study was designed and 120 reconstruction plans were reproduced using computed tomography images with oral surgeons. The proposed automated planning model was quantitatively compared with both the existing model and the surgeons’ plans. Conclusion: The results show that the developed framework attains stable automated planning that agrees with the surgeons’ decisions. Significance: This method addresses trade-off problems between symmetric reconstruction and restoration of the native contour of the mandible